The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in the present disclosure and are not admitted to be prior art by inclusion in this section.
Spiking Neural Networks (SNNs) are regarded as the third generation of artificial neural networks (ANNs). In SNNs as well as ANNs, connections between units may be represented by a number called a weight value which can be positive or negative. The higher the weight value, the stronger the connection. These connections may be referred to as input synapses as they may correspond to the way actual neurons influence one another across biological synapses. It has been shown that in SNNs, efficient learning has been enabled by algorithms or learning rules that rely on stochasticity in neurons' membrane potentials and input synapse weights. In existing solutions, however, stochasticity in SNNs is enabled using standard complementary metal-oxide semiconductor (CMOS) hardware which has large area and power overhead.